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InternVL

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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([4.2852e-01, 1.8430e-02, 5.5025e-01, 9.3998e-04, 1.8403e-04, 5.9318e-04,
1.5463e-04, 9.3382e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([4.2852e-01, 1.8430e-02, 5.5025e-01, 9.3998e-04, 1.8403e-04, 5.9318e-04,
1.5463e-04, 9.3382e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.4285, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5502, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0212, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([8.9550e-01, 1.2774e-02, 1.8986e-03, 8.8665e-02, 6.1585e-04, 2.5609e-04,
2.5635e-04, 2.9695e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([8.9550e-01, 1.2774e-02, 1.8986e-03, 8.8665e-02, 6.1585e-04, 2.5609e-04,
2.5635e-04, 2.9695e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.9842, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0158, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([8.2661e-01, 1.7283e-01, 8.8543e-06, 6.4983e-05, 1.9260e-04, 8.9433e-05,
1.8870e-04, 1.1030e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.2661e-01, 1.7283e-01, 8.8543e-06, 6.4983e-05, 1.9260e-04, 8.9433e-05,
1.8870e-04, 1.1030e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.1728, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8266, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([0.2319, 0.0944, 0.1572, 0.1743, 0.0856, 0.1411, 0.0403, 0.0751],
device='cuda:0', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2319, 0.0944, 0.1572, 0.1743, 0.0856, 0.1411, 0.0403, 0.0751],
device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-23 14:55:15,550] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.47 | optimizer_gradients: 0.26 | optimizer_step: 0.32
[2024-10-23 14:55:15,551] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9074.80 | backward_microstep: 8758.43 | backward_inner_microstep: 8752.51 | backward_allreduce_microstep: 5.73 | step_microstep: 7.71
[2024-10-23 14:55:15,551] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9074.81 | backward: 8758.42 | backward_inner: 8752.61 | backward_allreduce: 5.57 | step: 7.73
1%| | 54/4844 [13:59<21:38:58, 16.27s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many humans are holding cell phones in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many sleeping dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many green and yellow balloons are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Are there triangular pennants on display in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many green and yellow balloons are in the image?'], responses:['2']
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
[['2', '3', '4', '1', '5', '8', '7', '29']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['How many sleeping dogs are in the image?'], responses:['1']
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
[['1', '3', '4', '8', '6', '12', '2', '47']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
tensor([7.8387e-01, 7.2911e-02, 1.5283e-02, 1.2021e-01, 4.8155e-03, 1.2545e-03,
1.5613e-03, 9.3363e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.8387e-01, 7.2911e-02, 1.5283e-02, 1.2021e-01, 4.8155e-03, 1.2545e-03,
1.5613e-03, 9.3363e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many baboons are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many humans are holding cell phones in the image?'], responses:['3']
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
question: ['Are there triangular pennants on display in the image?'], responses:['no']
question: ['How many baboons are in the image?'], responses:['1']
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
[['1', '3', '4', '8', '6', '12', '2', '47']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
tensor([9.7087e-01, 2.8135e-03, 8.0611e-04, 3.2522e-04, 4.5923e-04, 3.9990e-04,
2.4305e-02, 1.9733e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************